The present invention relates to a classifier for sensor data, and more particularly to a method and apparatus for quickly and accurately classifying objects using sensor data.
Methods of remotely sensing objects are well known. Such methods include, for example, radar, sonar, and the use of infrared, acoustic and optical sensing devices. In most radar applications, the only properties of the target (i.e., the object being sensed) that are measured are its radial velocity and its location in range and angle, so that the only thing known about the target is its physical presence in space. However, it is possible to extract more information about the target which would permit the radar to distinguish one type of target from another, such as distinguishing between a tank and a truck. This capability is known as target classification.
Techniques for obtaining data of sufficient quality to permit target classification are well known in the art, and include, for example, High-range-resolution (HRR) with polarization diversity, engine modulations, cross-section fluctuations, synthetic aperture radar (SAR), and others. These well-known techniques are described, for example, in the textbook M. Skolnik, "Introduction to Radar Systems" Second Edition 434-440 (McGraw-Hill Publishing Company 1980), as well as in D. Wehner, "High Resolution Radar" (Artech House 1987). The entire texts of the Skolnik and Wehner publications are incorporated herein by reference.
In addition to collecting sensed data of appropriate quality, target classification requires that some method of signal recognition or pattern recognition be applied to be able to correctly estimate the type of target. An algorithm or apparatus that performs this analysis is called a "classifier".
It is a goal to devise an optimal classifier algorithm within the cost, time, weight and price constraints of the system, as well as one that can classify targets as accurately as the statistical uncertainties of the sensor data will allow. Such uncertainties can arise, for example, from small changes in radar-to-target viewing aspect angle, clutter, interference, fluctuations in target return due to scattering or reflecting centers, and unknown a priori knowledge of actual targets. In practice, however, the design of an "optimal" classifier is often unknown, generally because of lack of information about what particular statistical distribution the actual target data will follow. Furthermore, processing limitations imposed by the choice of computer architecture or available time or weight or power may require compromises in classifier design that prevent one from even approaching the results that could be achieved by an "optimal" classifier. Consequently, the lack of knowledge about what constitutes an "optimal" classifier design and the need to design around computer limitations both present major obstacles which must be overcome in designing an appropriate classifier for a particular sensor.
A number of classifiers are presently known in the art. One of these, called a correlation or profile matching classifier, operates by comparing sensor data from an unknown target to a battery of stored data "profiles" of known targets. The known target profile which most highly correlates with the unknown target data is finally selected as the output of the classifier. That is, "training" data vectors C.sub.1.sup.1, C.sub.2.sup.1, . . . , C.sub.N.sup.1 exist for class 1, and data vectors C.sub.1.sup.2, C.sub.2.sup.2, . . . , C.sub.N.sup.2 exist for class 2. Typically, these vectors are normalized by converting C.sub.j.sup.i to C.sub.j.sup.i /.parallel.C.sub.j.sup.i .parallel., where .parallel. .parallel. indicates the Euclidean norm. Consequently, the resulting training vectors all have norm one. Then, for an input vector C, one classifies C as a class 1 object if ##EQU1## where C.sup.t. is the transpose of the vector C.
Another type of classifier that is well-known in the art is called a quadratic classifier, which mathematically analyzes HRR profiles of the unknown target to determine the classification of the target based on a quadratic discriminant function. This type of well-known classifier is described, for example, in R. O. Duda & P. E. Hart, "Pattern Classification and Scene Analysis" pp. 22-32 (John Wiley & Sons 1973), which is incorporated herein by reference.
Classification accuracy has been improved, in the prior art, by the use of a sensor fusion classifier. This technique involves obtaining the outputs from independent sensors, each sensing the same target. The independent sensors may be of different types (e.g., one radar and one infrared), or they may be of the same type so long as the data collected from one sensor is independent of the data collected from any other sensor (e.g., by having each sensor collect data from a unique aspect angle). Each sensor's output data is then run through a classifier (e.g., correlation or quadratic) to produce two independently determined estimates of the target's classification. For example, the output of one classifier, operating on a first sensor's data, may conclude that the target is a tank. Furthermore, the output of a second classifier, operating on a second sensor's data, may conclude that that same target is a truck. For each of these estimates, a statistical level of confidence, usually based on a calculation of probability, is calculated, and the most confident target classification estimate is selected as the output of the sensor fusion classifier. This technique has yielded a 6-10% improvement in classification accuracy, compared with a quadratic classifier.
It is important, for proper functioning of the prior art sensor fusion classifier, that the input target data be collected from two independent sensors. This is necessary because meaningful differences in target classification estimates using prior art classifiers can only be generated when the data that is supplied to one of the classifiers is independent of the data that is supplied to the other classifier. Attempts to simply supply the same sensor data to two different types of classifiers (i.e., one correlation classifier and one quadratic classifier) have only resulted in classification estimates of comparable confidence from the two classifiers, yielding no improvement in accuracy, and needlessly increasing computation time.
A major drawback to the use of each of the above classifiers is the fact that each needs to perform highly complex mathematical operations on a great deal of data in order to generate accurate results. Sensors, such as radar, are capable of generating data very quickly, so the information necessary for accurately classifying a target can be obtained in real time. However, the goal of real time target classification is impeded by the fact that processors that satisfy the typical cost, weight, power and size requirements are incapable of running any of the classifier algorithms fast enough to keep up with the ongoing data stream. This problem is especially acute for correlation classifiers, which can require many times more computations than quadratic classifiers. Consequently, compromises have been made which reduce the amount of computation needed to produce an output. For example, it is possible to have a correlation classifier compare the unknown target data to a smaller number of known target profiles. However, this necessarily leads to a comparable degradation in target classification accuracy. Similar compromises with quadratic classifiers involve decreasing the dimension of the test vector by a factor of, for example, two, which will cut the number of required computations by approximately a factor of four. However, some classifier accuracy may also be lost.